Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, Liaoning, China.
Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, Liaoning, China.
Comput Biol Med. 2023 Oct;165:107388. doi: 10.1016/j.compbiomed.2023.107388. Epub 2023 Aug 26.
Colorectal Cancer (CRC) is currently one of the most common and deadly cancers. CRC is the third most common malignancy and the fourth leading cause of cancer death worldwide. It ranks as the second most frequent cause of cancer-related deaths in the United States and other developed countries. Histopathological images contain sufficient phenotypic information, they play an indispensable role in the diagnosis and treatment of CRC. In order to improve the objectivity and diagnostic efficiency for image analysis of intestinal histopathology, Computer-aided Diagnosis (CAD) methods based on machine learning (ML) are widely applied in image analysis of intestinal histopathology. In this investigation, we conduct a comprehensive study on recent ML-based methods for image analysis of intestinal histopathology. First, we discuss commonly used datasets from basic research studies with knowledge of intestinal histopathology relevant to medicine. Second, we introduce traditional ML methods commonly used in intestinal histopathology, as well as deep learning (DL) methods. Then, we provide a comprehensive review of the recent developments in ML methods for segmentation, classification, detection, and recognition, among others, for histopathological images of the intestine. Finally, the existing methods have been studied, and the application prospects of these methods in this field are given.
结直肠癌（CRC）是目前最常见和最致命的癌症之一。CRC 是全球第三大常见恶性肿瘤，也是癌症死亡的第四大主要原因。它是美国和其他发达国家癌症相关死亡的第二大常见原因。组织病理学图像包含足够的表型信息，在 CRC 的诊断和治疗中起着不可或缺的作用。为了提高对肠组织病理学图像分析的客观性和诊断效率，基于机器学习（ML）的计算机辅助诊断（CAD）方法广泛应用于肠组织病理学图像分析。在本研究中，我们对基于 ML 的肠组织病理学图像分析的最新方法进行了全面研究。首先，我们讨论了与医学相关的肠组织病理学基础知识研究中常用的数据集。其次，我们介绍了在肠组织病理学中常用的传统 ML 方法以及深度学习（DL）方法。然后，我们全面回顾了 ML 方法在肠组织病理学图像的分割、分类、检测和识别等方面的最新进展。最后，研究了现有的方法，并给出了这些方法在该领域的应用前景。
